Overview

Dataset statistics

Number of variables18
Number of observations8950
Missing cells314
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 MiB
Average record size in memory199.0 B

Variable types

Text1
Numeric17

Alerts

BALANCE is highly overall correlated with BALANCE_FREQUENCY and 4 other fieldsHigh correlation
BALANCE_FREQUENCY is highly overall correlated with BALANCE and 1 other fieldsHigh correlation
CASH_ADVANCE is highly overall correlated with BALANCE and 2 other fieldsHigh correlation
CASH_ADVANCE_FREQUENCY is highly overall correlated with BALANCE and 2 other fieldsHigh correlation
CASH_ADVANCE_TRX is highly overall correlated with BALANCE and 2 other fieldsHigh correlation
INSTALLMENTS_PURCHASES is highly overall correlated with PURCHASES and 3 other fieldsHigh correlation
MINIMUM_PAYMENTS is highly overall correlated with BALANCE and 1 other fieldsHigh correlation
ONEOFF_PURCHASES is highly overall correlated with ONEOFF_PURCHASES_FREQUENCY and 2 other fieldsHigh correlation
ONEOFF_PURCHASES_FREQUENCY is highly overall correlated with ONEOFF_PURCHASES and 2 other fieldsHigh correlation
PURCHASES is highly overall correlated with INSTALLMENTS_PURCHASES and 5 other fieldsHigh correlation
PURCHASES_FREQUENCY is highly overall correlated with INSTALLMENTS_PURCHASES and 3 other fieldsHigh correlation
PURCHASES_INSTALLMENTS_FREQUENCY is highly overall correlated with INSTALLMENTS_PURCHASES and 3 other fieldsHigh correlation
PURCHASES_TRX is highly overall correlated with INSTALLMENTS_PURCHASES and 5 other fieldsHigh correlation
MINIMUM_PAYMENTS has 313 (3.5%) missing valuesMissing
CUST_ID has unique valuesUnique
PURCHASES has 2044 (22.8%) zerosZeros
ONEOFF_PURCHASES has 4302 (48.1%) zerosZeros
INSTALLMENTS_PURCHASES has 3916 (43.8%) zerosZeros
CASH_ADVANCE has 4628 (51.7%) zerosZeros
PURCHASES_FREQUENCY has 2043 (22.8%) zerosZeros
ONEOFF_PURCHASES_FREQUENCY has 4302 (48.1%) zerosZeros
PURCHASES_INSTALLMENTS_FREQUENCY has 3915 (43.7%) zerosZeros
CASH_ADVANCE_FREQUENCY has 4628 (51.7%) zerosZeros
CASH_ADVANCE_TRX has 4628 (51.7%) zerosZeros
PURCHASES_TRX has 2044 (22.8%) zerosZeros
PAYMENTS has 240 (2.7%) zerosZeros
PRC_FULL_PAYMENT has 5903 (66.0%) zerosZeros

Reproduction

Analysis started2026-02-04 16:17:32.312978
Analysis finished2026-02-04 16:17:51.779484
Duration19.47 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

CUST_ID
Text

Unique 

Distinct8950
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size550.8 KiB
2026-02-04T17:17:51.896482image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters53700
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8950 ?
Unique (%)100.0%

Sample

1st rowC10001
2nd rowC10002
3rd rowC10003
4th rowC10004
5th rowC10005
ValueCountFrequency (%)
c100071
 
< 0.1%
c191901
 
< 0.1%
c100011
 
< 0.1%
c100021
 
< 0.1%
c100031
 
< 0.1%
c100041
 
< 0.1%
c191751
 
< 0.1%
c191761
 
< 0.1%
c191771
 
< 0.1%
c191781
 
< 0.1%
Other values (8940)8940
99.9%
2026-02-04T17:17:52.076134image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
112672
23.6%
C8950
16.7%
03737
 
7.0%
23652
 
6.8%
33651
 
6.8%
53642
 
6.8%
73640
 
6.8%
43636
 
6.8%
63633
 
6.8%
83633
 
6.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)53700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
112672
23.6%
C8950
16.7%
03737
 
7.0%
23652
 
6.8%
33651
 
6.8%
53642
 
6.8%
73640
 
6.8%
43636
 
6.8%
63633
 
6.8%
83633
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)53700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
112672
23.6%
C8950
16.7%
03737
 
7.0%
23652
 
6.8%
33651
 
6.8%
53642
 
6.8%
73640
 
6.8%
43636
 
6.8%
63633
 
6.8%
83633
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)53700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
112672
23.6%
C8950
16.7%
03737
 
7.0%
23652
 
6.8%
33651
 
6.8%
53642
 
6.8%
73640
 
6.8%
43636
 
6.8%
63633
 
6.8%
83633
 
6.8%

BALANCE
Real number (ℝ)

High correlation 

Distinct8871
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1564.4748
Minimum0
Maximum19043.139
Zeros80
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2026-02-04T17:17:52.139892image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.8145184
Q1128.28192
median873.38523
Q32054.14
95-th percentile5909.1118
Maximum19043.139
Range19043.139
Interquartile range (IQR)1925.8581

Descriptive statistics

Standard deviation2081.5319
Coefficient of variation (CV)1.3304988
Kurtosis7.6747513
Mean1564.4748
Median Absolute Deviation (MAD)799.8652
Skewness2.393386
Sum14002050
Variance4332775
MonotonicityNot monotonic
2026-02-04T17:17:52.215502image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
080
 
0.9%
1146.6693641
 
< 0.1%
757.4702011
 
< 0.1%
1253.1883171
 
< 0.1%
5058.2996351
 
< 0.1%
296.9059441
 
< 0.1%
1084.6526471
 
< 0.1%
237.1984421
 
< 0.1%
1546.4146161
 
< 0.1%
1213.5513381
 
< 0.1%
Other values (8861)8861
99.0%
ValueCountFrequency (%)
080
0.9%
0.0001991
 
< 0.1%
0.0011461
 
< 0.1%
0.0012141
 
< 0.1%
0.0012891
 
< 0.1%
0.0048161
 
< 0.1%
0.0066511
 
< 0.1%
0.0096841
 
< 0.1%
0.019681
 
< 0.1%
0.0211021
 
< 0.1%
ValueCountFrequency (%)
19043.138561
< 0.1%
18495.558551
< 0.1%
16304.889251
< 0.1%
16259.448571
< 0.1%
16115.59641
< 0.1%
15532.339721
< 0.1%
15258.22591
< 0.1%
15244.748651
< 0.1%
15155.532861
< 0.1%
14581.459141
< 0.1%

BALANCE_FREQUENCY
Real number (ℝ)

High correlation 

Distinct43
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.87727073
Minimum0
Maximum1
Zeros80
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2026-02-04T17:17:52.290202image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.272727
Q10.888889
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.111111

Descriptive statistics

Standard deviation0.236904
Coefficient of variation (CV)0.27004663
Kurtosis3.0923696
Mean0.87727073
Median Absolute Deviation (MAD)0
Skewness-2.0232655
Sum7851.573
Variance0.056123506
MonotonicityNot monotonic
2026-02-04T17:17:52.368927image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
16211
69.4%
0.909091410
 
4.6%
0.818182278
 
3.1%
0.727273223
 
2.5%
0.545455219
 
2.4%
0.636364209
 
2.3%
0.454545172
 
1.9%
0.363636170
 
1.9%
0.272727151
 
1.7%
0.181818146
 
1.6%
Other values (33)761
 
8.5%
ValueCountFrequency (%)
080
0.9%
0.09090967
0.7%
0.18
 
0.1%
0.1111115
 
0.1%
0.1259
 
0.1%
0.1428577
 
0.1%
0.1666677
 
0.1%
0.181818146
1.6%
0.29
 
0.1%
0.2222225
 
0.1%
ValueCountFrequency (%)
16211
69.4%
0.909091410
 
4.6%
0.955
 
0.6%
0.88888953
 
0.6%
0.87557
 
0.6%
0.85714351
 
0.6%
0.83333360
 
0.7%
0.818182278
 
3.1%
0.820
 
0.2%
0.77777822
 
0.2%

PURCHASES
Real number (ℝ)

High correlation  Zeros 

Distinct6203
Distinct (%)69.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1003.2048
Minimum0
Maximum49039.57
Zeros2044
Zeros (%)22.8%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2026-02-04T17:17:52.446110image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q139.635
median361.28
Q31110.13
95-th percentile3998.6195
Maximum49039.57
Range49039.57
Interquartile range (IQR)1070.495

Descriptive statistics

Standard deviation2136.6348
Coefficient of variation (CV)2.1298091
Kurtosis111.38877
Mean1003.2048
Median Absolute Deviation (MAD)361.28
Skewness8.1442691
Sum8978683.3
Variance4565208.2
MonotonicityNot monotonic
2026-02-04T17:17:52.524308image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02044
 
22.8%
45.6527
 
0.3%
15016
 
0.2%
6016
 
0.2%
10013
 
0.1%
30013
 
0.1%
20013
 
0.1%
45012
 
0.1%
12010
 
0.1%
7010
 
0.1%
Other values (6193)6776
75.7%
ValueCountFrequency (%)
02044
22.8%
0.014
 
< 0.1%
0.051
 
< 0.1%
0.241
 
< 0.1%
0.71
 
< 0.1%
12
 
< 0.1%
1.41
 
< 0.1%
21
 
< 0.1%
4.441
 
< 0.1%
4.81
 
< 0.1%
ValueCountFrequency (%)
49039.571
< 0.1%
41050.41
< 0.1%
40040.711
< 0.1%
38902.711
< 0.1%
35131.161
< 0.1%
32539.781
< 0.1%
31299.351
< 0.1%
27957.681
< 0.1%
27790.421
< 0.1%
26784.621
< 0.1%

ONEOFF_PURCHASES
Real number (ℝ)

High correlation  Zeros 

Distinct4014
Distinct (%)44.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean592.43737
Minimum0
Maximum40761.25
Zeros4302
Zeros (%)48.1%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2026-02-04T17:17:52.598690image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median38
Q3577.405
95-th percentile2671.094
Maximum40761.25
Range40761.25
Interquartile range (IQR)577.405

Descriptive statistics

Standard deviation1659.8879
Coefficient of variation (CV)2.8017948
Kurtosis164.18757
Mean592.43737
Median Absolute Deviation (MAD)38
Skewness10.045083
Sum5302314.5
Variance2755227.9
MonotonicityNot monotonic
2026-02-04T17:17:52.677582image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04302
48.1%
45.6546
 
0.5%
5017
 
0.2%
20015
 
0.2%
10013
 
0.1%
6013
 
0.1%
7012
 
0.1%
15012
 
0.1%
100012
 
0.1%
25011
 
0.1%
Other values (4004)4497
50.2%
ValueCountFrequency (%)
04302
48.1%
0.017
 
0.1%
0.022
 
< 0.1%
0.051
 
< 0.1%
0.241
 
< 0.1%
0.71
 
< 0.1%
14
 
< 0.1%
1.42
 
< 0.1%
21
 
< 0.1%
4.991
 
< 0.1%
ValueCountFrequency (%)
40761.251
< 0.1%
40624.061
< 0.1%
34087.731
< 0.1%
33803.841
< 0.1%
26547.431
< 0.1%
26514.321
< 0.1%
25122.771
< 0.1%
24543.521
< 0.1%
23032.971
< 0.1%
22257.391
< 0.1%

INSTALLMENTS_PURCHASES
Real number (ℝ)

High correlation  Zeros 

Distinct4452
Distinct (%)49.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean411.06764
Minimum0
Maximum22500
Zeros3916
Zeros (%)43.8%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2026-02-04T17:17:52.751279image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median89
Q3468.6375
95-th percentile1750.0875
Maximum22500
Range22500
Interquartile range (IQR)468.6375

Descriptive statistics

Standard deviation904.33812
Coefficient of variation (CV)2.199974
Kurtosis96.575178
Mean411.06764
Median Absolute Deviation (MAD)89
Skewness7.2991199
Sum3679055.4
Variance817827.43
MonotonicityNot monotonic
2026-02-04T17:17:52.825210image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03916
43.8%
30014
 
0.2%
20014
 
0.2%
10014
 
0.2%
15012
 
0.1%
12511
 
0.1%
759
 
0.1%
2258
 
0.1%
5008
 
0.1%
3508
 
0.1%
Other values (4442)4936
55.2%
ValueCountFrequency (%)
03916
43.8%
1.951
 
< 0.1%
4.441
 
< 0.1%
4.81
 
< 0.1%
6.331
 
< 0.1%
7.261
 
< 0.1%
7.671
 
< 0.1%
9.281
 
< 0.1%
9.581
 
< 0.1%
9.651
 
< 0.1%
ValueCountFrequency (%)
225001
< 0.1%
15497.191
< 0.1%
14686.11
< 0.1%
13184.431
< 0.1%
12738.471
< 0.1%
12560.851
< 0.1%
125411
< 0.1%
123751
< 0.1%
12235.051
< 0.1%
12128.941
< 0.1%

CASH_ADVANCE
Real number (ℝ)

High correlation  Zeros 

Distinct4323
Distinct (%)48.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean978.87111
Minimum0
Maximum47137.212
Zeros4628
Zeros (%)51.7%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2026-02-04T17:17:52.898256image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31113.8211
95-th percentile4647.1691
Maximum47137.212
Range47137.212
Interquartile range (IQR)1113.8211

Descriptive statistics

Standard deviation2097.1639
Coefficient of variation (CV)2.1424311
Kurtosis52.899434
Mean978.87111
Median Absolute Deviation (MAD)0
Skewness5.1666091
Sum8760896.5
Variance4398096.3
MonotonicityNot monotonic
2026-02-04T17:17:52.974275image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04628
51.7%
197.9155861
 
< 0.1%
1430.4853331
 
< 0.1%
211.4859811
 
< 0.1%
364.4891871
 
< 0.1%
1168.4931881
 
< 0.1%
2462.1007891
 
< 0.1%
191.3765351
 
< 0.1%
521.6643691
 
< 0.1%
38.6905521
 
< 0.1%
Other values (4313)4313
48.2%
ValueCountFrequency (%)
04628
51.7%
14.2222161
 
< 0.1%
18.0427681
 
< 0.1%
18.1179671
 
< 0.1%
18.1234131
 
< 0.1%
18.1266831
 
< 0.1%
18.1499461
 
< 0.1%
18.2045771
 
< 0.1%
18.2406261
 
< 0.1%
18.2800431
 
< 0.1%
ValueCountFrequency (%)
47137.211761
< 0.1%
29282.109151
< 0.1%
27296.485761
< 0.1%
26268.699891
< 0.1%
26194.049541
< 0.1%
23130.821061
< 0.1%
22665.77851
< 0.1%
21943.849421
< 0.1%
20712.670081
< 0.1%
20277.331121
< 0.1%

PURCHASES_FREQUENCY
Real number (ℝ)

High correlation  Zeros 

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49035055
Minimum0
Maximum1
Zeros2043
Zeros (%)22.8%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2026-02-04T17:17:53.052454image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.083333
median0.5
Q30.916667
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.833334

Descriptive statistics

Standard deviation0.40137075
Coefficient of variation (CV)0.81853839
Kurtosis-1.6386309
Mean0.49035055
Median Absolute Deviation (MAD)0.416667
Skewness0.060164236
Sum4388.6374
Variance0.16109848
MonotonicityNot monotonic
2026-02-04T17:17:53.126796image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
12178
24.3%
02043
22.8%
0.083333677
 
7.6%
0.916667396
 
4.4%
0.5395
 
4.4%
0.166667392
 
4.4%
0.833333373
 
4.2%
0.333333367
 
4.1%
0.25345
 
3.9%
0.583333316
 
3.5%
Other values (37)1468
16.4%
ValueCountFrequency (%)
02043
22.8%
0.083333677
 
7.6%
0.09090943
 
0.5%
0.127
 
0.3%
0.11111118
 
0.2%
0.12532
 
0.4%
0.14285726
 
0.3%
0.166667392
 
4.4%
0.18181816
 
0.2%
0.219
 
0.2%
ValueCountFrequency (%)
12178
24.3%
0.916667396
 
4.4%
0.90909128
 
0.3%
0.924
 
0.3%
0.88888918
 
0.2%
0.87526
 
0.3%
0.85714325
 
0.3%
0.833333373
 
4.2%
0.81818221
 
0.2%
0.89
 
0.1%

ONEOFF_PURCHASES_FREQUENCY
Real number (ℝ)

High correlation  Zeros 

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.20245768
Minimum0
Maximum1
Zeros4302
Zeros (%)48.1%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2026-02-04T17:17:53.198748image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.083333
Q30.3
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.29833607
Coefficient of variation (CV)1.4735725
Kurtosis1.1618456
Mean0.20245768
Median Absolute Deviation (MAD)0.083333
Skewness1.5356128
Sum1811.9963
Variance0.089004408
MonotonicityNot monotonic
2026-02-04T17:17:53.273073image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
04302
48.1%
0.0833331104
 
12.3%
0.166667592
 
6.6%
1481
 
5.4%
0.25418
 
4.7%
0.333333355
 
4.0%
0.416667244
 
2.7%
0.5235
 
2.6%
0.583333197
 
2.2%
0.666667167
 
1.9%
Other values (37)855
 
9.6%
ValueCountFrequency (%)
04302
48.1%
0.0833331104
 
12.3%
0.09090956
 
0.6%
0.139
 
0.4%
0.11111126
 
0.3%
0.12541
 
0.5%
0.14285737
 
0.4%
0.166667592
 
6.6%
0.18181834
 
0.4%
0.227
 
0.3%
ValueCountFrequency (%)
1481
5.4%
0.916667151
 
1.7%
0.9090914
 
< 0.1%
0.91
 
< 0.1%
0.8888892
 
< 0.1%
0.8756
 
0.1%
0.8571431
 
< 0.1%
0.833333120
 
1.3%
0.81818210
 
0.1%
0.84
 
< 0.1%

PURCHASES_INSTALLMENTS_FREQUENCY
Real number (ℝ)

High correlation  Zeros 

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.36443734
Minimum0
Maximum1
Zeros3915
Zeros (%)43.7%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2026-02-04T17:17:53.345045image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.166667
Q30.75
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation0.39744778
Coefficient of variation (CV)1.0905792
Kurtosis-1.3986322
Mean0.36443734
Median Absolute Deviation (MAD)0.166667
Skewness0.50920116
Sum3261.7142
Variance0.15796474
MonotonicityNot monotonic
2026-02-04T17:17:53.418269image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
03915
43.7%
11331
 
14.9%
0.416667388
 
4.3%
0.916667345
 
3.9%
0.833333311
 
3.5%
0.5310
 
3.5%
0.166667305
 
3.4%
0.666667292
 
3.3%
0.75291
 
3.3%
0.083333275
 
3.1%
Other values (37)1187
 
13.3%
ValueCountFrequency (%)
03915
43.7%
0.083333275
 
3.1%
0.09090912
 
0.1%
0.16
 
0.1%
0.1111119
 
0.1%
0.1255
 
0.1%
0.1428576
 
0.1%
0.166667305
 
3.4%
0.18181814
 
0.2%
0.29
 
0.1%
ValueCountFrequency (%)
11331
14.9%
0.916667345
 
3.9%
0.90909125
 
0.3%
0.919
 
0.2%
0.88888928
 
0.3%
0.87528
 
0.3%
0.85714330
 
0.3%
0.833333311
 
3.5%
0.81818221
 
0.2%
0.818
 
0.2%

CASH_ADVANCE_FREQUENCY
Real number (ℝ)

High correlation  Zeros 

Distinct54
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1351442
Minimum0
Maximum1.5
Zeros4628
Zeros (%)51.7%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2026-02-04T17:17:53.489701image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.222222
95-th percentile0.583333
Maximum1.5
Range1.5
Interquartile range (IQR)0.222222

Descriptive statistics

Standard deviation0.20012139
Coefficient of variation (CV)1.4807989
Kurtosis3.3347343
Mean0.1351442
Median Absolute Deviation (MAD)0
Skewness1.8286863
Sum1209.5406
Variance0.04004857
MonotonicityNot monotonic
2026-02-04T17:17:53.565814image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04628
51.7%
0.0833331021
 
11.4%
0.166667759
 
8.5%
0.25578
 
6.5%
0.333333439
 
4.9%
0.416667273
 
3.1%
0.5215
 
2.4%
0.583333142
 
1.6%
0.666667125
 
1.4%
0.09090970
 
0.8%
Other values (44)700
 
7.8%
ValueCountFrequency (%)
04628
51.7%
0.0833331021
 
11.4%
0.09090970
 
0.8%
0.139
 
0.4%
0.11111129
 
0.3%
0.12547
 
0.5%
0.14285749
 
0.5%
0.166667759
 
8.5%
0.18181842
 
0.5%
0.221
 
0.2%
ValueCountFrequency (%)
1.51
 
< 0.1%
1.251
 
< 0.1%
1.1666672
 
< 0.1%
1.1428571
 
< 0.1%
1.1251
 
< 0.1%
1.11
 
< 0.1%
1.0909091
 
< 0.1%
125
0.3%
0.91666727
0.3%
0.9090913
 
< 0.1%

CASH_ADVANCE_TRX
Real number (ℝ)

High correlation  Zeros 

Distinct65
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2488268
Minimum0
Maximum123
Zeros4628
Zeros (%)51.7%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2026-02-04T17:17:53.641580image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile15
Maximum123
Range123
Interquartile range (IQR)4

Descriptive statistics

Standard deviation6.8246467
Coefficient of variation (CV)2.1006496
Kurtosis61.646862
Mean3.2488268
Median Absolute Deviation (MAD)0
Skewness5.7212982
Sum29077
Variance46.575803
MonotonicityNot monotonic
2026-02-04T17:17:53.720541image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04628
51.7%
1887
 
9.9%
2620
 
6.9%
3436
 
4.9%
4384
 
4.3%
5308
 
3.4%
6246
 
2.7%
7205
 
2.3%
8171
 
1.9%
10150
 
1.7%
Other values (55)915
 
10.2%
ValueCountFrequency (%)
04628
51.7%
1887
 
9.9%
2620
 
6.9%
3436
 
4.9%
4384
 
4.3%
5308
 
3.4%
6246
 
2.7%
7205
 
2.3%
8171
 
1.9%
9111
 
1.2%
ValueCountFrequency (%)
1233
< 0.1%
1101
 
< 0.1%
1071
 
< 0.1%
931
 
< 0.1%
801
 
< 0.1%
711
 
< 0.1%
691
 
< 0.1%
631
 
< 0.1%
623
< 0.1%
611
 
< 0.1%

PURCHASES_TRX
Real number (ℝ)

High correlation  Zeros 

Distinct173
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.709832
Minimum0
Maximum358
Zeros2044
Zeros (%)22.8%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2026-02-04T17:17:53.794859image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median7
Q317
95-th percentile57
Maximum358
Range358
Interquartile range (IQR)16

Descriptive statistics

Standard deviation24.857649
Coefficient of variation (CV)1.6898662
Kurtosis34.7931
Mean14.709832
Median Absolute Deviation (MAD)7
Skewness4.6306553
Sum131653
Variance617.90272
MonotonicityNot monotonic
2026-02-04T17:17:53.870162image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02044
22.8%
1667
 
7.5%
12570
 
6.4%
2379
 
4.2%
6352
 
3.9%
3314
 
3.5%
4285
 
3.2%
7275
 
3.1%
8267
 
3.0%
5267
 
3.0%
Other values (163)3530
39.4%
ValueCountFrequency (%)
02044
22.8%
1667
 
7.5%
2379
 
4.2%
3314
 
3.5%
4285
 
3.2%
5267
 
3.0%
6352
 
3.9%
7275
 
3.1%
8267
 
3.0%
9248
 
2.8%
ValueCountFrequency (%)
3581
< 0.1%
3471
< 0.1%
3441
< 0.1%
3091
< 0.1%
3081
< 0.1%
2981
< 0.1%
2741
< 0.1%
2731
< 0.1%
2541
< 0.1%
2482
< 0.1%

CREDIT_LIMIT
Real number (ℝ)

Distinct205
Distinct (%)2.3%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean4494.4495
Minimum50
Maximum30000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2026-02-04T17:17:53.942779image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile1000
Q11600
median3000
Q36500
95-th percentile12000
Maximum30000
Range29950
Interquartile range (IQR)4900

Descriptive statistics

Standard deviation3638.8157
Coefficient of variation (CV)0.80962435
Kurtosis2.8366559
Mean4494.4495
Median Absolute Deviation (MAD)1800
Skewness1.522464
Sum40220828
Variance13240980
MonotonicityNot monotonic
2026-02-04T17:17:54.019615image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3000784
 
8.8%
1500722
 
8.1%
1200621
 
6.9%
1000614
 
6.9%
2500612
 
6.8%
4000506
 
5.7%
6000463
 
5.2%
5000389
 
4.3%
2000371
 
4.1%
7500277
 
3.1%
Other values (195)3590
40.1%
ValueCountFrequency (%)
501
 
< 0.1%
1505
 
0.1%
2003
 
< 0.1%
30014
 
0.2%
4003
 
< 0.1%
4506
 
0.1%
500121
1.4%
60021
 
0.2%
6501
 
< 0.1%
70020
 
0.2%
ValueCountFrequency (%)
300002
 
< 0.1%
280001
 
< 0.1%
250001
 
< 0.1%
230002
 
< 0.1%
225001
 
< 0.1%
220001
 
< 0.1%
215002
 
< 0.1%
210002
 
< 0.1%
205001
 
< 0.1%
2000010
0.1%

PAYMENTS
Real number (ℝ)

Zeros 

Distinct8711
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1733.1439
Minimum0
Maximum50721.483
Zeros240
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2026-02-04T17:17:54.096072image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile89.988924
Q1383.27617
median856.90155
Q31901.1343
95-th percentile6082.0906
Maximum50721.483
Range50721.483
Interquartile range (IQR)1517.8582

Descriptive statistics

Standard deviation2895.0638
Coefficient of variation (CV)1.6704117
Kurtosis54.770736
Mean1733.1439
Median Absolute Deviation (MAD)581.35146
Skewness5.9076198
Sum15511637
Variance8381394.2
MonotonicityNot monotonic
2026-02-04T17:17:54.172915image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0240
 
2.7%
221.7982911
 
< 0.1%
846.7879171
 
< 0.1%
826.0367481
 
< 0.1%
2571.5732141
 
< 0.1%
1903.2796431
 
< 0.1%
454.8885061
 
< 0.1%
956.0287471
 
< 0.1%
4560.775721
 
< 0.1%
1757.6871361
 
< 0.1%
Other values (8701)8701
97.2%
ValueCountFrequency (%)
0240
2.7%
0.0495131
 
< 0.1%
0.0564661
 
< 0.1%
2.3895831
 
< 0.1%
3.5005051
 
< 0.1%
4.5235551
 
< 0.1%
4.8415431
 
< 0.1%
5.0707261
 
< 0.1%
9.0400171
 
< 0.1%
9.5333131
 
< 0.1%
ValueCountFrequency (%)
50721.483361
< 0.1%
46930.598241
< 0.1%
40627.595241
< 0.1%
39461.96581
< 0.1%
39048.597621
< 0.1%
36066.750681
< 0.1%
35843.625931
< 0.1%
34107.074991
< 0.1%
33994.727851
< 0.1%
33486.310441
< 0.1%

MINIMUM_PAYMENTS
Real number (ℝ)

High correlation  Missing 

Distinct8636
Distinct (%)> 99.9%
Missing313
Missing (%)3.5%
Infinite0
Infinite (%)0.0%
Mean864.20654
Minimum0.019163
Maximum76406.208
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2026-02-04T17:17:54.688047image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Quantile statistics

Minimum0.019163
5-th percentile73.282006
Q1169.12371
median312.34395
Q3825.48546
95-th percentile2766.5633
Maximum76406.208
Range76406.188
Interquartile range (IQR)656.36175

Descriptive statistics

Standard deviation2372.4466
Coefficient of variation (CV)2.745231
Kurtosis283.98999
Mean864.20654
Median Absolute Deviation (MAD)190.3741
Skewness13.622797
Sum7464151.9
Variance5628502.9
MonotonicityNot monotonic
2026-02-04T17:17:54.767558image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
299.3518812
 
< 0.1%
342.286491
 
< 0.1%
229.4114181
 
< 0.1%
271.5281691
 
< 0.1%
6404.8554841
 
< 0.1%
616.8625441
 
< 0.1%
211.9841931
 
< 0.1%
324.9547471
 
< 0.1%
250.8718111
 
< 0.1%
150.3171431
 
< 0.1%
Other values (8626)8626
96.4%
(Missing)313
 
3.5%
ValueCountFrequency (%)
0.0191631
< 0.1%
0.0377441
< 0.1%
0.055881
< 0.1%
0.0594811
< 0.1%
0.1170361
< 0.1%
0.2619841
< 0.1%
0.3119531
< 0.1%
0.3194751
< 0.1%
1.1130271
< 0.1%
1.3340751
< 0.1%
ValueCountFrequency (%)
76406.207521
< 0.1%
61031.61861
< 0.1%
56370.041171
< 0.1%
50260.759471
< 0.1%
43132.728231
< 0.1%
42629.551171
< 0.1%
38512.124771
< 0.1%
31871.363791
< 0.1%
30528.43241
< 0.1%
29019.802881
< 0.1%

PRC_FULL_PAYMENT
Real number (ℝ)

Zeros 

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15371465
Minimum0
Maximum1
Zeros5903
Zeros (%)66.0%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2026-02-04T17:17:54.843059image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.142857
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.142857

Descriptive statistics

Standard deviation0.2924992
Coefficient of variation (CV)1.9028713
Kurtosis2.4323953
Mean0.15371465
Median Absolute Deviation (MAD)0
Skewness1.9428199
Sum1375.7461
Variance0.08555578
MonotonicityNot monotonic
2026-02-04T17:17:54.918020image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
05903
66.0%
1488
 
5.5%
0.083333426
 
4.8%
0.166667166
 
1.9%
0.25156
 
1.7%
0.5156
 
1.7%
0.090909153
 
1.7%
0.333333134
 
1.5%
0.194
 
1.1%
0.283
 
0.9%
Other values (37)1191
 
13.3%
ValueCountFrequency (%)
05903
66.0%
0.083333426
 
4.8%
0.090909153
 
1.7%
0.194
 
1.1%
0.11111161
 
0.7%
0.12552
 
0.6%
0.14285754
 
0.6%
0.166667166
 
1.9%
0.18181875
 
0.8%
0.283
 
0.9%
ValueCountFrequency (%)
1488
5.5%
0.91666777
 
0.9%
0.90909119
 
0.2%
0.916
 
0.2%
0.88888912
 
0.1%
0.87518
 
0.2%
0.85714312
 
0.1%
0.83333363
 
0.7%
0.81818217
 
0.2%
0.833
 
0.4%

TENURE
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.517318
Minimum6
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2026-02-04T17:17:54.971168image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile8
Q112
median12
Q312
95-th percentile12
Maximum12
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.3383308
Coefficient of variation (CV)0.11620159
Kurtosis7.6948232
Mean11.517318
Median Absolute Deviation (MAD)0
Skewness-2.9430173
Sum103080
Variance1.7911292
MonotonicityNot monotonic
2026-02-04T17:17:55.019265image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
127584
84.7%
11365
 
4.1%
10236
 
2.6%
6204
 
2.3%
8196
 
2.2%
7190
 
2.1%
9175
 
2.0%
ValueCountFrequency (%)
6204
 
2.3%
7190
 
2.1%
8196
 
2.2%
9175
 
2.0%
10236
 
2.6%
11365
 
4.1%
127584
84.7%
ValueCountFrequency (%)
127584
84.7%
11365
 
4.1%
10236
 
2.6%
9175
 
2.0%
8196
 
2.2%
7190
 
2.1%
6204
 
2.3%

Interactions

2026-02-04T17:17:50.455566image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:32.695176image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:33.828557image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:34.976738image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:35.954929image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:37.157327image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:38.100587image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:39.261835image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:40.252468image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:41.251990image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:42.486209image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:43.555367image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:44.577179image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:45.901050image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:46.940835image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:47.940678image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:49.054765image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:50.519112image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:32.763895image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:33.895970image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:35.036292image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:36.017939image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:37.215085image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:38.159073image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:39.323459image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
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2026-02-04T17:17:37.878113image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:39.032710image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:40.022851image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:41.014670image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:42.243725image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:43.312197image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:44.333763image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:45.650611image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:46.692804image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:47.710227image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:48.792005image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:50.210628image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:51.305124image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:33.633576image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:34.800632image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:35.784125image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:36.971009image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:37.932667image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:39.088129image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:40.078396image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:41.070500image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:42.302483image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:43.371212image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:44.394380image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:45.710918image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:46.753146image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:47.765421image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:48.856512image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:50.270409image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:51.366409image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:33.701077image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:34.861144image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:35.844419image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:37.036927image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:37.991954image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:39.149168image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:40.141090image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:41.134222image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:42.367055image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:43.436343image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:44.457784image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:45.779228image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:46.819353image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:47.826334image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:48.925698image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:50.337152image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:51.423950image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:33.764662image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:34.917675image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:35.898779image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
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2026-02-04T17:17:44.515584image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:45.842937image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:46.881130image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:47.884161image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:48.991497image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-04T17:17:50.395819image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Correlations

2026-02-04T17:17:55.079073image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
BALANCEBALANCE_FREQUENCYCASH_ADVANCECASH_ADVANCE_FREQUENCYCASH_ADVANCE_TRXCREDIT_LIMITINSTALLMENTS_PURCHASESMINIMUM_PAYMENTSONEOFF_PURCHASESONEOFF_PURCHASES_FREQUENCYPAYMENTSPRC_FULL_PAYMENTPURCHASESPURCHASES_FREQUENCYPURCHASES_INSTALLMENTS_FREQUENCYPURCHASES_TRXTENURE
BALANCE1.0000.5450.5660.5440.5490.372-0.0900.9000.1460.1200.432-0.4840.006-0.145-0.144-0.0460.066
BALANCE_FREQUENCY0.5451.0000.1370.1770.1760.1060.1280.5020.1350.1590.207-0.1740.1480.2020.1520.2030.229
CASH_ADVANCE0.5660.1371.0000.9410.9520.163-0.3570.482-0.185-0.1890.257-0.266-0.385-0.454-0.378-0.408-0.113
CASH_ADVANCE_FREQUENCY0.5440.1770.9411.0000.9830.088-0.3660.456-0.179-0.1760.203-0.287-0.391-0.453-0.382-0.407-0.131
CASH_ADVANCE_TRX0.5490.1760.9520.9831.0000.097-0.3570.472-0.175-0.1740.215-0.281-0.384-0.447-0.374-0.399-0.099
CREDIT_LIMIT0.3720.1060.1630.0880.0971.0000.1230.2640.3050.2820.4490.0210.2610.1040.0470.1900.170
INSTALLMENTS_PURCHASES-0.0900.128-0.357-0.366-0.3570.1231.000-0.0520.2000.1850.2390.2760.7060.7860.9230.7840.125
MINIMUM_PAYMENTS0.9000.5020.4820.4560.4720.264-0.0521.0000.0700.0510.368-0.478-0.008-0.104-0.085-0.0250.137
ONEOFF_PURCHASES0.1460.135-0.185-0.179-0.1750.3050.2000.0701.0000.9520.3630.0490.7510.4240.1170.5900.096
ONEOFF_PURCHASES_FREQUENCY0.1200.159-0.189-0.176-0.1740.2820.1850.0510.9521.0000.3210.0610.6930.4630.1120.6070.084
PAYMENTS0.4320.2070.2570.2030.2150.4490.2390.3680.3630.3211.0000.1870.3950.1720.1210.2840.205
PRC_FULL_PAYMENT-0.484-0.174-0.266-0.287-0.2810.0210.276-0.4780.0490.0610.1871.0000.2380.2920.2590.2530.020
PURCHASES0.0060.148-0.385-0.391-0.3840.2610.706-0.0080.7510.6930.3950.2381.0000.7950.6060.8850.133
PURCHASES_FREQUENCY-0.1450.202-0.454-0.453-0.4470.1040.786-0.1040.4240.4630.1720.2920.7951.0000.8520.9240.098
PURCHASES_INSTALLMENTS_FREQUENCY-0.1440.152-0.378-0.382-0.3740.0470.923-0.0850.1170.1120.1210.2590.6060.8521.0000.7810.114
PURCHASES_TRX-0.0460.203-0.408-0.407-0.3990.1900.784-0.0250.5900.6070.2840.2530.8850.9240.7811.0000.169
TENURE0.0660.229-0.113-0.131-0.0990.1700.1250.1370.0960.0840.2050.0200.1330.0980.1140.1691.000

Missing values

2026-02-04T17:17:51.529307image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
A simple visualization of nullity by column.
2026-02-04T17:17:51.640404image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2026-02-04T17:17:51.744301image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CUST_IDBALANCEBALANCE_FREQUENCYPURCHASESONEOFF_PURCHASESINSTALLMENTS_PURCHASESCASH_ADVANCEPURCHASES_FREQUENCYONEOFF_PURCHASES_FREQUENCYPURCHASES_INSTALLMENTS_FREQUENCYCASH_ADVANCE_FREQUENCYCASH_ADVANCE_TRXPURCHASES_TRXCREDIT_LIMITPAYMENTSMINIMUM_PAYMENTSPRC_FULL_PAYMENTTENURE
0C1000140.9007490.81818295.400.0095.400.0000000.1666670.0000000.0833330.000000021000.0201.802084139.5097870.00000012
1C100023202.4674160.9090910.000.000.006442.9454830.0000000.0000000.0000000.250000407000.04103.0325971072.3402170.22222212
2C100032495.1488621.000000773.17773.170.000.0000001.0000001.0000000.0000000.0000000127500.0622.066742627.2847870.00000012
3C100041666.6705420.6363641499.001499.000.00205.7880170.0833330.0833330.0000000.083333117500.00.000000NaN0.00000012
4C10005817.7143351.00000016.0016.000.000.0000000.0833330.0833330.0000000.000000011200.0678.334763244.7912370.00000012
5C100061809.8287511.0000001333.280.001333.280.0000000.6666670.0000000.5833330.000000081800.01400.0577702407.2460350.00000012
6C10007627.2608061.0000007091.016402.63688.380.0000001.0000001.0000001.0000000.00000006413500.06354.314328198.0658941.00000012
7C100081823.6527431.000000436.200.00436.200.0000001.0000000.0000001.0000000.0000000122300.0679.065082532.0339900.00000012
8C100091014.9264731.000000861.49661.49200.000.0000000.3333330.0833330.2500000.000000057000.0688.278568311.9634090.00000012
9C10010152.2259750.5454551281.601281.600.000.0000000.1666670.1666670.0000000.0000000311000.01164.770591100.3022620.00000012
CUST_IDBALANCEBALANCE_FREQUENCYPURCHASESONEOFF_PURCHASESINSTALLMENTS_PURCHASESCASH_ADVANCEPURCHASES_FREQUENCYONEOFF_PURCHASES_FREQUENCYPURCHASES_INSTALLMENTS_FREQUENCYCASH_ADVANCE_FREQUENCYCASH_ADVANCE_TRXPURCHASES_TRXCREDIT_LIMITPAYMENTSMINIMUM_PAYMENTSPRC_FULL_PAYMENTTENURE
8940C19181130.8385541.000000591.240.00591.240.0000001.0000000.0000000.8333330.000000061000.0475.52326282.7713201.006
8941C191825967.4752700.833333214.550.00214.558555.4093260.8333330.0000000.6666670.6666671359000.0966.202912861.9499060.006
8942C1918340.8297491.000000113.280.00113.280.0000001.0000000.0000000.8333330.000000061000.094.48882886.2831010.256
8943C191845.8717120.50000020.9020.900.000.0000000.1666670.1666670.0000000.00000001500.058.64488343.4737170.006
8944C19185193.5717220.8333331012.731012.730.000.0000000.3333330.3333330.0000000.000000024000.00.000000NaN0.006
8945C1918628.4935171.000000291.120.00291.120.0000001.0000000.0000000.8333330.000000061000.0325.59446248.8863650.506
8946C1918719.1832151.000000300.000.00300.000.0000001.0000000.0000000.8333330.000000061000.0275.861322NaN0.006
8947C1918823.3986730.833333144.400.00144.400.0000000.8333330.0000000.6666670.000000051000.081.27077582.4183690.256
8948C1918913.4575640.8333330.000.000.0036.5587780.0000000.0000000.0000000.16666720500.052.54995955.7556280.256
8949C19190372.7080750.6666671093.251093.250.00127.0400080.6666670.6666670.0000000.3333332231200.063.16540488.2889560.006